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Single-cell profiling reveals a memory B cell-like subtype of follicular lymphoma with increased transformation risk

Author

Listed:
  • Xuehai Wang

    (BC Cancer Agency)

  • Michael Nissen

    (BC Cancer Agency)

  • Deanne Gracias

    (BC Cancer Agency)

  • Manabu Kusakabe

    (BC Cancer Agency)

  • Guillermo Simkin

    (BC Cancer Agency)

  • Aixiang Jiang

    (BC Cancer Agency)

  • Gerben Duns

    (BC Cancer Agency)

  • Clementine Sarkozy

    (BC Cancer Agency
    Institut Gustave Roussy)

  • Laura Hilton

    (BC Cancer Agency)

  • Elizabeth A. Chavez

    (BC Cancer Agency)

  • Gabriela C. Segat

    (BC Cancer Agency)

  • Rachel Wong

    (BC Cancer Agency)

  • Jubin Kim

    (BC Cancer Agency)

  • Tomohiro Aoki

    (BC Cancer Agency)

  • Rashedul Islam

    (BC Cancer Agency)

  • Christina May

    (BC Cancer Agency)

  • Stacy Hung

    (BC Cancer Agency)

  • Kate Tyshchenko

    (BC Cancer Agency)

  • Ryan R. Brinkman

    (BC Cancer Agency)

  • Martin Hirst

    (BC Cancer Agency)

  • Aly Karsan

    (BC Cancer Agency)

  • Ciara Freeman

    (BC Cancer Agency)

  • Laurie H. Sehn

    (BC Cancer Agency)

  • Ryan D. Morin

    (BC Cancer Agency
    Simon Fraser University)

  • Andrew J. Roth

    (BC Cancer Agency)

  • Kerry J. Savage

    (BC Cancer Agency)

  • Jeffrey W. Craig

    (BC Cancer Agency)

  • Sohrab P. Shah

    (BC Cancer Agency
    Memorial Sloan Kettering Cancer Center)

  • Christian Steidl

    (BC Cancer Agency)

  • David W. Scott

    (BC Cancer Agency)

  • Andrew P. Weng

    (BC Cancer Agency)

Abstract

Follicular lymphoma (FL) is an indolent cancer of mature B-cells but with ongoing risk of transformation to more aggressive histology over time. Recurrent mutations associated with transformation have been identified; however, prognostic features that can be discerned at diagnosis could be clinically useful. We present here comprehensive profiling of both tumor and immune compartments in 155 diagnostic FL biopsies at single-cell resolution by mass cytometry. This revealed a diversity of phenotypes but included two recurrent patterns, one which closely resembles germinal center B-cells (GCB) and another which appears more related to memory B-cells (MB). GCB-type tumors are enriched for EZH2, TNFRSF14, and MEF2B mutations, while MB-type tumors contain increased follicular helper T-cells. MB-type and intratumoral phenotypic diversity are independently associated with increased risk of transformation, supporting biological relevance of these features. Notably, a reduced 26-marker panel retains sufficient information to allow phenotypic profiling of future cohorts by conventional flow cytometry.

Suggested Citation

  • Xuehai Wang & Michael Nissen & Deanne Gracias & Manabu Kusakabe & Guillermo Simkin & Aixiang Jiang & Gerben Duns & Clementine Sarkozy & Laura Hilton & Elizabeth A. Chavez & Gabriela C. Segat & Rachel , 2022. "Single-cell profiling reveals a memory B cell-like subtype of follicular lymphoma with increased transformation risk," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34408-0
    DOI: 10.1038/s41467-022-34408-0
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    References listed on IDEAS

    as
    1. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    2. Griffith, Daniel M. & Veech, Joseph A. & Marsh, Charles J., 2016. "cooccur: Probabilistic Species Co-Occurrence Analysis in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 69(c02).
    3. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
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